For example, I have a bunch of points on a map. In particular, one point has a concentration of 5151. I've classified my symbology so that the cutoff is at 1000-5000 and 5000-10000, yet the color being displayed on this one point is the color of 1000-5000. Is it just the way GIS interprets the data with the points around this particular point?
Is there any work around?
Are you sure that there is only one point at that location? Perhaps there are 2 and you are just seeing the one that is "on top".
Use the Advanced symbology and symbol order to fix if this is the case.
I think I understand what you are saying, but I don't think that is it (I don't see any other points on top anyway). I geoprocessed specifically on that layer which includes that point in question (turned the layer off and the point disappeared).
Just wondering if there are in fact 2 point there. And various interpolators deal with coincident data points in different ways.
But the most common is to just randomly use 1 of them. You will never know which one.
The value that is interpolated at a location does not necessarily get retained. That is a property of the interpolator. Suffice to say, I wouln't worry about it, but if you have to you need to use a different interpolator, which may not be the best overall interpolator for your area. Should you need more information, you can start here.
and the protracted discussion of interpolators in the
Wow that's a lot of information. I'll definitely read in to this. Do you have any recommendations for my situation before I spend too much time? Essentially I have a bunch of wells with different concentrations of a contaminant around a site (but since these are wells, they are only concentrations at a specific point). I want to interpolate the concentration for the area between each well.
Therein lies the problem. Interpolation tries to provide a value for the intervening space between point observations. Whatever interpolator is being used for a given point pattern and observation at those points will determine how an interpolator behaves. If!!! there is a spatial pattern in the data and the observation points are well spread out and sufficient to cover the area, then 'most' interpolators will return 'similar' results... note the quotes.
Your question was a bit confusing since you chose a natural neighbor interpolator which will produce a smoother surface (generally) than something like IDW, but inherent to it will be the fact/chance that the value returned by the interpolator and the actual observation will be the same. I suspected that you were more concerned by that issue... at point XY, the observed and calculated were different. This does not mean that the interpolation was bad, it just means that the surroundin data points included with point XY yielded that value.
Kriging in some of its forms, allow you to evaluate the underlying properties of 'how well' the interpolator worked for your data. It is comprehensive, yet it may not yield any better interpolation than other methods.
Now back to a plausible case... if you have a purely random phenomenon observed at locations in space ... no interpolator in the world is going to help and any 'calculated' value in the void between observations are pure conjecture, since the phenomenon is by definition, random.
So in summary... know your data, use the interpolator that best represents that data then decide if preserving the value at observation points is more important than having a 'calculated value' at those locations be different. You can get an interpolator that preserves the observed values, but looks ridiculous or you can have a cool looking interpolation that doesn't represent the observed points well or at all.
Sorry for the lecture, but interpolation should come with big warning labels